Massive MIMO, also known as large-scale MIMO, is a scalable version of point-to-point MIMO, or multiuser MIMO, with many antennas at both link ends. Multiuser detection for massive MIMO relies on the implementation of linear signal processing schemes such as the zero-forcing (ZF) and the linear minimum mean-square-error (LMMSE) detectors.
An option for implementing the linear (e.g., LMMSE) receiver is via an exact implementation. The cheapest, computationally speaking, implementation of an exact linear procedure is based on the Cholesky decomposition, namely:                1. First, decompose A≙LLT, where L is a lower triangular matrix.        2. Next, solve Ly=b via forward substitution to extract the vector y.        3. Finally, solve LTx=y by back substitution to extract the desired soft decision x*.        
The main drawback of such an exact computation approach is in it being computationally-heavy, where the number of required operations is cubic with the number of transmitting antennas, which again may be large in massive MIMO applications. Another problem with this approach, deteriorating its hardware (HW) efficiency, is the fact that its pipeline latency is relatively large, since the Cholesky solver is based on a pipeline of 3 different stages (again the decomposition itself, forward and backward substitution) that must be performed sequentially, one after the other. Consequently, the supported data throughput, which is proportional to the latency reciprocal, is also limited.
A popular iterative alternative is based on the classical Gauss-Seidel (GS) method:{circumflex over (x)}t+1=(D+L)−1(b−U{circumflex over (x)}t),where D, L, U are the diagonal, strictly lower triangular and strictly upper triangular matrices of the (LMMSE) matrix A, respectively.